Empirical model-building and response surface
Empirical model-building and response surface
Experimental design issues in simulation with examples from semiconductor manufacturing
WSC '92 Proceedings of the 24th conference on Winter simulation
Designing simulation experiments for evaluating manufacturing systems
WSC '94 Proceedings of the 26th conference on Winter simulation
Response surface methodology and its application in simulation
WSC '93 Proceedings of the 25th conference on Winter simulation
Statistical analysis of simulation output
Proceedings of the 29th conference on Winter simulation
A review of simulation optimization techniques
Proceedings of the 30th conference on Winter simulation
Proceedings of the 30th conference on Winter simulation
Designing simulation experiments
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
New advances for wedding optimization and simulation
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Simulation with Arena
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Design and Analysis of Experiments
Design and Analysis of Experiments
Experimental design for simulation: experimental design for simulation
Proceedings of the 35th conference on Winter simulation: driving innovation
Sensitivity analysis of spatially aggregated responses: A gradient-based method
International Journal of Geographical Information Science
Identifying significant factors affecting request for information (RFI) process time
Proceedings of the 40th Conference on Winter Simulation
Exchange rates and trade tariffs assessment for strategic decisions in supply networks configuration
Proceedings of the Winter Simulation Conference
Simulation optimization for emergency department resources allocation
Proceedings of the Winter Simulation Conference
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This tutorial introduces some of the ideas, issues, challenges, solutions, and opportunities in deciding how to experiment with a simulation model to learn about its behavior. Careful planning, or designing, of simulation experiments is generally a great help, saving time and effort by providing efficient ways to estimate the effects of changes in the model's inputs on its outputs. Traditional experimental-design methods are discussed in the context of simulation experiments, as are the broader questions pertaining to planning computer-simulation experiments.